Go Perceptron

A single / multi level perceptron classifier with weights estimated from sonar training data set using stochastic gradient descent. Recently I added back propagation algorithm over multilayer perceptron network.
The implementation is in dev. Planned features:

complete future features XD (see above)

find co-workers

create a ml library in openqasm (just kidding)

brainstorming / devtesting other an models

Updates

2017-08-08: Introduced multi layer perceptron network definition with parametric number of hidden layer and neurons. Back propagation algorithm with different transfer function actived - I wanna thank you dakk because I was truly inspired by your code.

Run test

You can setup a MultiLayerPerceptron using PrepareMLPNet. The first parameter, a simple []int, define the entire network struct. Example:

[4, 3, 3] will define a network struct with 3 layer: input, hidden, output, with respectively 4, 3 and 3 neurons. For classification problems the input layers has to be define with a number of neurons that match features of pattern shown to network. Of course, the output layer should have a number of unit equals to the number of class in training set.
The network will have this topology: